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     "name": "#%% md\n"
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   "source": [
    "<a href=\"https://www.youtube.com/watch?v=EHtHF9Kvm0Y&list=PLxqBkZuBynVTn2lkHNAcw6lgm1MD5QiMK&index=28&ab_channel=Rohan-Paul-AI\"><h1 style=\"font-size:250%; font-family:cursive; color:#ff6666;\"><b>Link YouTube Video - LongFormer with Tensorflow | NER</b></h1></a>\n",
    "\n",
    "[![IMAGE ALT TEXT](https://imgur.com/h7u2oId.png)](https://www.youtube.com/watch?v=EHtHF9Kvm0Y&list=PLxqBkZuBynVTn2lkHNAcw6lgm1MD5QiMK&index=28&ab_channel=Rohan-Paul-AI \"\")\n",
    "\n",
    "\n",
    "---------------------\n",
    "\n",
    "## [Kaggle - Feedback Prize - Evaluating Student Writing](https://www.kaggle.com/competitions/feedback-prize-2021)\n",
    "\n",
    "### You can run this Notebook either Locally or in Kaggle - Just modify the 'ROOT_DIR' variable to properly refer to the dataset"
   ]
  },
  {
   "attachments": {},
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   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
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   "source": [
    "## The classification Problem in this competition\n",
    "\n",
    "Basically, we have a bunch of essays written by kids in the age range of about 12-18 years old in which we have to find word sequences that can be classified as one of 7 \"discourse types\", which are\n",
    "\n",
    "* Lead - an introduction that begins with a statistic, a quotation, a description, or some other device to grab the reader’s attention and point toward the thesis\n",
    "  Interestingly, this is not present in all the the training set data. Around 40% of the ids do not have a lead!\n",
    "\n",
    "* Position - an opinion or conclusion on the main question\n",
    "  Almost all the training data has a Position. However, it is not always at the beginning. \n",
    "\n",
    "* Claim - a claim that supports the position\n",
    "  Almost all the training data has a Claim. One training set data has 12 Claims!! Over half have at least 3 claims\n",
    "\n",
    "* Counterclaim - a claim that refutes another claim or gives an opposing reason to the position\n",
    "\n",
    "* Rebuttal - a claim that refutes a counterclaim\n",
    "\n",
    "* Evidence - ideas or examples that support claims, counterclaims, or rebuttals.\n",
    "\n",
    "* Concluding Statement - a concluding statement that restates the claims\n",
    "\n",
    "So basically, we are tasked with giving feedback on argumentative essays written by students. Specifically, our task is to predict the human annotations.\n",
    "\n",
    "This annotation will be done in 2 steps:\n",
    "\n",
    "### - Segment each essay into discrete, rhetorical and argumentative elements (i.e., discourse elements) .\n",
    "\n",
    "### - Classify each element as one of 7 \"discourse types\",\n",
    "\n",
    "## Basics on the training data\n",
    "\n",
    "We have a bunch of text files and a separate train.csv with labels. \n",
    "\n",
    "In the csv, we get reference to the text files, and then multiple lines per text file with spans indicating a specific discourse type.\n",
    "\n",
    "The Ground Truth here is a combination of the discourse type and the prediction string. \n",
    "\n",
    "Here, Kaggle provides labels as characters (discourse start, discourse end) or words (predictionstring). \n",
    "\n",
    "--------------\n",
    "\n",
    "train.csv - a .csv file containing the annotated version of all essays in the training set\n",
    "\n",
    "\n",
    "* **discourse_id** - discourse_id is a unique identifier for each discourse element. Basically, each row in train.csv contains one discourse element, and the discourse_id is what identifies each of these discourse elements.\n",
    "\n",
    "It is different to the 'id' column in that 'id' is the identifier of the full student essay that the discourse element came from. So multiple rows can have the same value for 'id'. On the other hand, each row in train.csv will have a different value for 'discourse_id', since each row is a different discourse element.\n",
    "\n",
    "Hope that helps and let me know if it's still unclear.\n",
    "\n",
    "* **discourse_start** - character position where discourse element begins in the essay response\n",
    "\n",
    "* **discourse_end** - character position where discourse element ends in the essay response\n",
    "\n",
    "* **discourse_text** - text of discourse element\n",
    "\n",
    "* **discourse_type** - classification of discourse element\n",
    "\n",
    "* **discourse_type_num** - enumerated class label of discourse element\n",
    "\n",
    "* **predictionstring** - the word indices of the training sample, as required for predictions\n",
    "\n",
    "The **predictionstring** corresponds to - the index of the words in the essay and the predicted discourse type for this sequence of words, SHOULD be correct. \n",
    "\n",
    "There can be partial matches, if the correct discourse type is predicted but on a longer or shorter sequence of words than specified in the Ground Truth.\n",
    "\n",
    "https://www.kaggle.com/code/erikbruin/nlp-on-student-writing-eda"
   ]
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    "------------------------------------\n",
    "\n",
    "## Evaluation Metric - Overlap Concept in this competition\n",
    "\n",
    "### 1. As per the Evaluation page - \"If the overlap between the ground truth and prediction is >= 0.5, and the overlap between the prediction and the ground truth >= 0.5, the prediction is a match and considered a true positive. If multiple matches exist, the match with the highest pair of overlaps is taken.\"\n",
    "\n",
    "The **\"overlap between the ground truth and prediction\"** is a measure of how many words in the ground truth match the predicted words. It is calculated as the number of words that appear in both the ground truth and the prediction divided by the total number of words in the ground truth. \n",
    "\n",
    "For example, if the ground truth for a sample contains 10 words and the model predicts 8 of those words correctly, the overlap between the ground truth and prediction would be 0.8 or 80%.\n",
    "\n",
    "On the other hand, the **\"overlap between the prediction and the ground truth\"** is a measure of how many words in the prediction match the words in the ground truth. It is calculated as the number of words that appear in both the ground truth and the prediction divided by the total number of words in the prediction. \n",
    "\n",
    "For example, if the model predicts 12 words for a sample and 10 of those words match the words in the ground truth, the overlap between the prediction and ground truth would be 0.83 or 83%.\n",
    "\n",
    "Example:\n",
    "Ground truth: 1 2 3 4 5\n",
    "Prediction: 1 2 3\n",
    "\n",
    "Common indices: 1 2 3 (3 common indices)\n",
    "\n",
    "- overlap between the ground truth and prediction: 3 common indices / 5 ground truth indices = 0.6 (>= 0.5)\n",
    "\n",
    "- overlap between the prediction and the ground truth: 3 common indices / 3 prediction indices = 1.0 (>= 0.5)\n",
    "\n",
    "Since both overlaps are greater than or equal to 0.5, this prediction is considered a match (true positive).\n",
    "\n",
    "### 2. In both, the numerator is the size of the intersection. Both calculations have to be at least 0.5 for the prediction to be considered a hit.\n",
    "\n",
    "These two measures are used together to determine whether a prediction is a true positive or not. \n",
    "\n",
    "By using both measures, the evaluation metric ensures that the predicted words are not only present in the ground truth, but also that the ground truth words are correctly identified by the model.\n",
    "\n",
    "In some cases, there might be multiple matches (multiple predictions with an overlap >= 0.5). If this happens, the competition rules state that the match with the highest pair of overlaps is considered.\n",
    "\n",
    "### 3. \"If multiple matches exist, the match with the highest pair of overlaps is taken\" - \n",
    "\n",
    "This statement means that if there are several predictions that match a single ground truth, only the prediction with the highest overlaps (for both ground truth and prediction) will be considered a true positive.\n",
    "\n",
    "Example:\n",
    "Ground truth: 1 2 3 4 5\n",
    "Prediction 1: 1 2 3\n",
    "Prediction 2: 1 2 3 4\n",
    "\n",
    "In this example, both predictions match the ground truth. However, Prediction 2 has a higher overlap and will be considered the true positive. Prediction 1 will not be considered.\n",
    "\n",
    "### 4. \"Any unmatched ground truths are false negatives and any unmatched predictions are false positives.\"\n",
    "\n",
    "\n",
    "This statement means that any ground truth without a corresponding matching prediction is considered a false negative (missed correct prediction), and any prediction without a corresponding matching ground truth is considered a false positive (incorrect prediction).\n",
    "\n",
    "Example:\n",
    "Ground truth: 1 2 3 4 5\n",
    "Prediction: 1 2\n",
    "\n",
    "Since the overlaps are not >= 0.5 for both ground truth and prediction, this prediction is considered a false positive. And since there's no match for the ground truth, it's considered a false negative.\n",
    "\n",
    "A basic implementation of the above concept will like below \n",
    "\n",
    "Below code is just for clarification of the concept, as for implementations for this project, I will use a slightly different version of this code later.\n",
    "\n",
    "```py\n",
    "\n",
    "def calculate_overlap(ground_truth, prediction):\n",
    "    ground_truth_set = set(ground_truth)\n",
    "    prediction_set = set(prediction)\n",
    "    \n",
    "    intersection = ground_truth_set.intersection(prediction_set)\n",
    "    overlap_ground_truth = len(intersection) / len(ground_truth_set)\n",
    "    overlap_prediction = len(intersection) / len(prediction_set)\n",
    "    \n",
    "    return overlap_ground_truth, overlap_prediction\n",
    "\n",
    "def evaluate_overlap(ground_truth, prediction):\n",
    "    overlap_ground_truth, overlap_prediction = calculate_overlap(ground_truth, prediction)\n",
    "    \n",
    "    if overlap_ground_truth >= 0.5 and overlap_prediction >= 0.5:\n",
    "        return \"True Positive\"\n",
    "    elif overlap_ground_truth < 0.5:\n",
    "        return \"False Negative\"\n",
    "    elif overlap_prediction < 0.5:\n",
    "        return \"False Positive\"\n",
    "\n",
    "# Example usage\n",
    "ground_truth = [1, 2, 3, 4, 5]\n",
    "prediction = [1, 2, 3]\n",
    "\n",
    "result = evaluate_overlap(ground_truth, prediction)\n",
    "print(result)  # Output: True Positive\n",
    "\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF version 2.10.1\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "\n",
    "import pandas as pd, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "from pathlib import Path\n",
    "import spacy\n",
    "from spacy import displacy\n",
    "\n",
    "from transformers import *\n",
    "\n",
    "from transformers import AutoTokenizer, AutoConfig, TFAutoModel\n",
    "\n",
    "from train import *\n",
    "from util import *\n",
    "\n",
    "print('TF version',tf.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# set number of gpus you wish to use. \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\" #0,1 for 2 gpus if you are using Kaggle's 2 GPUs\n",
    "\n",
    "# ROOT_DIR = '../input/feedback-prize-2021/' # Directory when running in Kaggle\n",
    "ROOT_DIR = '../input/' # local machine's directory \n",
    "\n",
    "MODEL_NAME = 'allenai/longformer-base-4096'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In tensorflow what is os.environ\\[\"CUDA\\_VISIBLE\\_DEVICES\"\\]\n",
    "\n",
    "`os.environ[\"CUDA_VISIBLE_DEVICES\"]` is an environment variable used in TensorFlow to specify which GPUs to use for computation.\n",
    "\n",
    "When a TensorFlow is run on a system with multiple GPUs, `os.environ[\"CUDA_VISIBLE_DEVICES\"]` can be set to a comma-separated list of integers representing the GPU IDs that the program is allowed to use.\n",
    "\n",
    "For example, if you have two GPUs and you want to use only the first GPU for computation, you can set `os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"` before running your TensorFlow program.\n",
    "\n",
    "This is useful in situations where you want to run multiple TensorFlow programs on the same system, each using a different set of GPUs, without having them interfere with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1251, 8)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>discourse_id</th>\n",
       "      <th>discourse_start</th>\n",
       "      <th>discourse_end</th>\n",
       "      <th>discourse_text</th>\n",
       "      <th>discourse_type</th>\n",
       "      <th>discourse_type_num</th>\n",
       "      <th>predictionstring</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>423A1CA112E2</td>\n",
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       "      <td>8</td>\n",
       "      <td>229</td>\n",
       "      <td>Modern humans today are always on their phone....</td>\n",
       "      <td>Lead</td>\n",
       "      <td>Lead 1</td>\n",
       "      <td>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>423A1CA112E2</td>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>230</td>\n",
       "      <td>312</td>\n",
       "      <td>They are some really bad consequences when stu...</td>\n",
       "      <td>Position</td>\n",
       "      <td>Position 1</td>\n",
       "      <td>45 46 47 48 49 50 51 52 53 54 55 56 57 58 59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>423A1CA112E2</td>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>313</td>\n",
       "      <td>401</td>\n",
       "      <td>Some certain areas in the United States ban ph...</td>\n",
       "      <td>Evidence</td>\n",
       "      <td>Evidence 1</td>\n",
       "      <td>60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>423A1CA112E2</td>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>402</td>\n",
       "      <td>758</td>\n",
       "      <td>When people have phones, they know about certa...</td>\n",
       "      <td>Evidence</td>\n",
       "      <td>Evidence 2</td>\n",
       "      <td>76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>423A1CA112E2</td>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>759</td>\n",
       "      <td>886</td>\n",
       "      <td>Driving is one of the way how to get around. P...</td>\n",
       "      <td>Claim</td>\n",
       "      <td>Claim 1</td>\n",
       "      <td>139 140 141 142 143 144 145 146 147 148 149 15...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  discourse_id  discourse_start  discourse_end  \\\n",
       "0  423A1CA112E2  1.622630e+12                8            229   \n",
       "1  423A1CA112E2  1.622630e+12              230            312   \n",
       "2  423A1CA112E2  1.622630e+12              313            401   \n",
       "3  423A1CA112E2  1.622630e+12              402            758   \n",
       "4  423A1CA112E2  1.622630e+12              759            886   \n",
       "\n",
       "                                      discourse_text discourse_type  \\\n",
       "0  Modern humans today are always on their phone....           Lead   \n",
       "1  They are some really bad consequences when stu...       Position   \n",
       "2  Some certain areas in the United States ban ph...       Evidence   \n",
       "3  When people have phones, they know about certa...       Evidence   \n",
       "4  Driving is one of the way how to get around. P...          Claim   \n",
       "\n",
       "  discourse_type_num                                   predictionstring  \n",
       "0             Lead 1  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1...  \n",
       "1         Position 1       45 46 47 48 49 50 51 52 53 54 55 56 57 58 59  \n",
       "2         Evidence 1    60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75  \n",
       "3         Evidence 2  76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9...  \n",
       "4            Claim 1  139 140 141 142 143 144 145 146 147 148 149 15...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(ROOT_DIR + 'train.csv')\n",
    "\n",
    "print( train.shape )\n",
    "train.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## First, lets check if the discourse_text and the predictionstring always have the same number of words (because ideally they should)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discourse_id</th>\n",
       "      <th>discourse_text</th>\n",
       "      <th>discourse_type</th>\n",
       "      <th>predictionstring</th>\n",
       "      <th>discourse_len</th>\n",
       "      <th>pred_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>Modern humans today are always on their phone....</td>\n",
       "      <td>Lead</td>\n",
       "      <td>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1...</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>They are some really bad consequences when stu...</td>\n",
       "      <td>Position</td>\n",
       "      <td>45 46 47 48 49 50 51 52 53 54 55 56 57 58 59</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>Some certain areas in the United States ban ph...</td>\n",
       "      <td>Evidence</td>\n",
       "      <td>60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>When people have phones, they know about certa...</td>\n",
       "      <td>Evidence</td>\n",
       "      <td>76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9...</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.622630e+12</td>\n",
       "      <td>Driving is one of the way how to get around. P...</td>\n",
       "      <td>Claim</td>\n",
       "      <td>139 140 141 142 143 144 145 146 147 148 149 15...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   discourse_id                                     discourse_text  \\\n",
       "0  1.622630e+12  Modern humans today are always on their phone....   \n",
       "1  1.622630e+12  They are some really bad consequences when stu...   \n",
       "2  1.622630e+12  Some certain areas in the United States ban ph...   \n",
       "3  1.622630e+12  When people have phones, they know about certa...   \n",
       "4  1.622630e+12  Driving is one of the way how to get around. P...   \n",
       "\n",
       "  discourse_type                                   predictionstring  \\\n",
       "0           Lead  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1...   \n",
       "1       Position       45 46 47 48 49 50 51 52 53 54 55 56 57 58 59   \n",
       "2       Evidence    60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75   \n",
       "3       Evidence  76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9...   \n",
       "4          Claim  139 140 141 142 143 144 145 146 147 148 149 15...   \n",
       "\n",
       "   discourse_len  pred_len  \n",
       "0             44        44  \n",
       "1             15        15  \n",
       "2             16        16  \n",
       "3             63        63  \n",
       "4             24        24  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"discourse_len\"] = train[\"discourse_text\"].apply(lambda x: len(x.split()))\n",
    "train[\"pred_len\"] = train[\"predictionstring\"].apply(lambda x: len(x.split()))\n",
    "\n",
    "\n",
    "cols_to_display = ['discourse_id', 'discourse_text', 'discourse_type','predictionstring', 'discourse_len', 'pred_len']\n",
    "train[cols_to_display].head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Below we find 468 instances of discourses where this equality is NOT matched (by one word)\n",
    "\n",
    "And you can find the reason in discussion topic: [Mystery Solved - Discrepancy Between PredictionString and DiscourseText](https://www.kaggle.com/c/feedback-prize-2021/discussion/297591)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of discourses is 1251\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discourse_id</th>\n",
       "      <th>discourse_text</th>\n",
       "      <th>discourse_type</th>\n",
       "      <th>predictionstring</th>\n",
       "      <th>discourse_len</th>\n",
       "      <th>pred_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1.622470e+12</td>\n",
       "      <td>if we would just make stricker laws for phone ...</td>\n",
       "      <td>Concluding Statement</td>\n",
       "      <td>303 304 305 306 307 308 309 310 311 312 313 31...</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>1.622560e+12</td>\n",
       "      <td>for navigation to wherever they are going</td>\n",
       "      <td>Claim</td>\n",
       "      <td>105 106 107 108 109 110 111 112</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      discourse_id                                     discourse_text  \\\n",
       "107   1.622470e+12  if we would just make stricker laws for phone ...   \n",
       "1025  1.622560e+12        for navigation to wherever they are going     \n",
       "\n",
       "            discourse_type                                   predictionstring  \\\n",
       "107   Concluding Statement  303 304 305 306 307 308 309 310 311 312 313 31...   \n",
       "1025                 Claim                    105 106 107 108 109 110 111 112   \n",
       "\n",
       "      discourse_len  pred_len  \n",
       "107              19        18  \n",
       "1025              7         8  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"The total number of discourses is {len(train)}\")\n",
    "\n",
    "train.query('discourse_len != pred_len')[cols_to_display]\n",
    "# The df.query function allows you to filter rows of the DataFrame based on a query expression written as a string. It provides a more concise and readable syntax compared to traditional boolean indexing and is particularly helpful when working with large DataFrames.\n",
    "# with the above line I filter rows of the DataFrame where the value in the 'discourse_len' column is not equal to the value in the 'pred_len' column.\n",
    "# The result will be a new DataFrame containing only the specified columns (i.e. `cols_to_display`) for the rows that meet the condition in the query function."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Length and frequency and relative position per discourse_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1 = train.groupby('discourse_type')['discourse_len'].mean().sort_values().plot(kind=\"barh\")\n",
    "ax1.set_title(\"Average number of words versus Discourse Type\", fontsize=14, fontweight = 'bold')\n",
    "ax1.set_xlabel(\"Average number of words\", fontsize = 10)\n",
    "ax1.set_ylabel(\"\")\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2 = train.groupby('discourse_type')['discourse_type'].count().sort_values().plot(kind=\"barh\")\n",
    "ax2.get_xaxis().set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ','))) #add thousands separator\n",
    "ax2.set_title(\"Frequency of Discourse Type in all essays\", fontsize=14, fontweight = 'bold')\n",
    "ax2.set_xlabel(\"Frequency\", fontsize = 10)\n",
    "ax2.set_ylabel(\"\")\n",
    "\n",
    "plt.tight_layout(pad=2)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Looking at the above graph, is there a correlation between the length of a discourse and the class (discourse_type)? Yes, there is. Evidence is the longest discourse type on average. \n",
    "\n",
    "When looking at the frequencies of occurence, we see that Counterclaim and Rebuttal are relatively rare"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Plotting Discourse type against Number of Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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           "caxis": {
            "gridcolor": "#DFE8F3",
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          "title": {
           "x": 0.05
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          "xaxis": {
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           "gridcolor": "#EBF0F8",
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            "standoff": 15
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           "zerolinewidth": 2
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        },
        "title": {
         "text": "Discourse Type Distribution ",
         "x": 0.5,
         "xanchor": "center",
         "y": 0.95,
         "yanchor": "top"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
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          "text": "Classes"
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        "yaxis": {
         "anchor": "x",
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         "title": {
          "text": "Number of Rows"
         }
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(x = np.unique(train[\"discourse_type\"]),\n",
    "\n",
    "y = [list(train[\"discourse_type\"]).count(i) for i in np.unique(train[\"discourse_type\"])] , \n",
    "            color = np.unique(train[\"discourse_type\"]),\n",
    "             color_continuous_scale=\"Emrld\") \n",
    "\n",
    "fig.update_xaxes(title=\"Classes\")\n",
    "\n",
    "fig.update_yaxes(title = \"Number of Rows\")\n",
    "\n",
    "fig.update_layout(showlegend = True,\n",
    "    title = {\n",
    "        'text': 'Discourse Type Distribution ',\n",
    "        'y':0.95,\n",
    "        'x':0.5,\n",
    "        'xanchor': 'center',\n",
    "        'yanchor': 'top'},\n",
    "        template=\"plotly_white\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Generate a visualization of a text file with annotated discourse segments, using Spacy and the `displacy.render` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<span class=\"tex2jax_ignore\"><h2 style=\"margin: 0\">66BD5DA864C8</h2>\n",
       "\n",
       "<div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">C\n",
       "<mark class=\"entity\" style=\"background: #8000ff; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    ell phone use while driving is one of those hot-button issues at the moment. Everyone has a cell phone. When it goes off while they are driving whether it is a message, social media or a phone call, curiosity seems to get the best of them and they feel as if they have to look now.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lead</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2b7ff6; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Using cell phones while driving should be banned completely for the safety of everyone on the road.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Position</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    It does not matter how good you are at multitasking, it should be forbidden and against the law entirely. \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "Here are just a few reasons for that.</br></br>\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Let's start with texts, social media and any other written phone alert. To stop and read this message or update you would have to take your eyes off of the road for longer than the recommended time of the one second, it takes to check the rearview mirror.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    It is extremely dangerous to type or read while driving. It is a hazard to everyone on the road, so it is not just your own life you are risking.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    As for making phone calls, this too should be banned. \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       "\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    I do understand that sometimes your spouse may call needing you to stop somewhere on the way home, or you think it may be an emergency. If that is the case, then pull over to the side of the road, take the call, and then return to driving. If the call can wait, return it later. If it can't wait, you can always stop briefly. There should be no reason to pick up your phone while driving your car.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Even hands-free devices that allow us to talk on the phone while driving with both hands on the wheel are still a hazard.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Studies have shown that while talking to someone on the phone your brain is putting a; lot of its energy into the conversation. This means that less of your brain is focused on driving the car. Yes, that could also mean a conversation with someone in the car is dangerous but you add in bad reception, how uncomfortable the hands-free earpieces are to wear and the annoyance of static or wind and you have a worse distraction.\n",
       "\n",
       "Whatever it is can almost always wait until you get where you are going. If it can't wait, you can always stop to look.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: d4dd80; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Cell phone use should be banned for the safety of everyone on the roads. It may not be as convenient, but we won't lose nearly as many teenage lives to Tweets or texts if they know their license could be taken if they do look.   \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Concluding Statement</span>\n",
       "</mark>\n",
       "</div></span>"
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       "<span class=\"tex2jax_ignore\"><h2 style=\"margin: 0\">2B49B48C3B2C</h2>\n",
       "\n",
       "<div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Hands Free Law</br></br>\n",
       "<mark class=\"entity\" style=\"background: #8000ff; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Losing the privilege to drive can be upsetting, all the work that's put in just to get the privilege to drive. Driving is a privilege that we earn and lose, this essay discusses the ways to lose and keep the privilege to drive. There are multiple ways to lose the privilege of driving but one way is by using a electronic device while behind the wheel. \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lead</span>\n",
       "</mark>\n",
       "\n",
       "<mark class=\"entity\" style=\"background: #2b7ff6; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Most people believe that drivers should not be able to use cell phones,texting,etc. while operating a vehicle.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Position</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Driving while talking on the phone can get others and the driver hurt\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       "\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    . One example could be hydroplaning. Hydroplaning is caused by traction and sliding on a film of water. Using one hand for driving and the other hand for the phone while it's raining is a creation of an accident and getting the privilege to drive taken away. Your driving privileges would be taken away because the driver has a phone in their hand and it's not allowed to have a phone in the vision of the driver behind the wheel.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Another example would be of course texting and driving because once again there is a phone in the hand of the driver instead of both hands on the wheel and no one paying attention to the road.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    This could lead to an accident because many drivers don't know where they're going and the car could lose control. Driving with a device in your hand is a distraction because your attention should be focusing on your surroundings and the vehicles on the road.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    To keep from getting hurt and jeopardizing your life and the life of others,drivers should follow the Hands Free Law. \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       "\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    The Hands Free Law is a proposed driving law that states that a driver shall not &quot;record or broadcast a video&quot; on any mobile phones, iPads, computers or touching any part of their bodies while operating a vehicle. Even with the Hands Free technology, drivers are not allowed to write, read, or send any messages or emails, but voice to text is allowed. Voice to text is a form of speech synthesis that converts text into spoken voice output or when the speaker can speak into their device and it translates their words into a text so that the speaker is able to answer the text message or any other important subject.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: d4dd80; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Driving is a privilege. Responsible drivers should always pay attention to the road and other drivers. Cell phones should always be out of the drivers hand so they may maneuver the steering wheel and not drive reckless. Drivers need to remember that driving is a privilege and that it can easily be taken away from you.       \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Concluding Statement</span>\n",
       "</mark>\n",
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       "<span class=\"tex2jax_ignore\"><h2 style=\"margin: 0\">8DB4751C05A1</h2>\n",
       "\n",
       "<div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">&quot;Phones and driving&quot;</br></br>\n",
       "<mark class=\"entity\" style=\"background: #8000ff; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Today's generation, phones are becoming an issue on the roads and putting people's lives at risk.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lead</span>\n",
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       "<mark class=\"entity\" style=\"background: #2b7ff6; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Phones should not be allowed to be touched or played on as you are on the roads so that you are fully aware of what's going on in your surroundings and also not taking your eyes off the roads.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Position</span>\n",
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       " \n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Texting while driving may ruin yours or another person's life.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
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       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    There are a lot of risks that could happen and put your life or someone else's lives in danger.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #ff8042; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Most people pick up the phone because they say it's important,\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Counterclaim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #ff0000; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    but even then the phone would be a distraction.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Rebuttal</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Take a moment to sit and think about how many accidents occur because of a text, now think about in old times when texting while driving was not a problem at all.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Authorities put down strict detailed laws about having your phone in your hand while driving.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Each offence doubles every time you get caught but this law was hardly effective because no one is taking the safer advice to insure the safety of yourself and others on the roads. The fines would turn into something more serious and if you got into a wreck, injuring another person you could more than likely spent some jail time.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: d4dd80; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    I know phones shouldn't be allowed to be messed with while you are driving because imagine if it were you in the situation and getting into a wreck all because someone was paying attention to their phone and not the road. You could lose it all in a blink of an eye or you could make someone else lose everything, don't be that person because either one there are causes and effects to your actions even if you didn't get hurt , you probably hurt someone else that has family that loves them like crazy.\n",
       "\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Concluding Statement</span>\n",
       "</mark>\n",
       "https://www. usatoday. com/story/news/nation-now/2018/07/02/georgia-hands-free-law-bans-holding-phone-while-driving/752752002/       </div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><h2 style=\"margin: 0\">3EE7CF68EFAA</h2>\n",
       "\n",
       "<div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #8000ff; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Using your phone while driving is dangerous.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lead</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2b7ff6; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    People should not be able to use a cellphone while driving because it is a hazard.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Position</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    People can die in car wrecks because they were on their phones while driving. People can get paralyzed by these car wrecks because of a cellphone.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    It is hypothesized that despite an effective way of communication, using cell phones during driving results in distracting attention of driver increasing road safety concerns.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Your main concern while your driving is that you obey the law and don't get in any wrecks. Your cellphone should not be your main concern while you are driving because it causes wrecks because your not paying attition to the road. Most wreck are because of people on their cellphones. About 421,000 people are injured in wrecks caused by texting and driving.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Texting and driving are the number one reason for wrecks in the US.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Texting and driving has its pros and cons.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    One pro is that you could get a text about an emergency and you could possibly save someones life. Another pro is that if your lost then you can look up where you have to go or get directions. Some of the cons are that you can get in wrecks, get injured, and even die because you were texting and driving. You should be able to be on your phone is there is bad traffic or if your at a red light.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Having your phone out while your driving is illegal now because of the risks of doing so\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       ".\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "     You cant even eat or drink while driving because of the hazards. Texting and driving is no joke. It is the number one cause for wrecks in the world and it can also take your life.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: d4dd80; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    I think that texting and driving should be illegal because if your lost or have to text somebody you can always pull over to the side of the road and use your phone because you can put not only your life at risk, but everyone else that is in the car as well.   \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Concluding Statement</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><h2 style=\"margin: 0\">6CC267680CD2</h2>\n",
       "\n",
       "<div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Phones &amp; Driving</br></br>\n",
       "<mark class=\"entity\" style=\"background: #8000ff; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    There is no doubt that with how advanced technology has become in the past few years or so people have really advanced the technological possibilities. People have now started to make some of the AI's (artificial intelligence) like Alexa and Google Home. There are even some cars that allow you to ask it questions in order to find out the weather or finding the fastest route to take to work. Along with all of these technological advancements comes the ability to hook the phone up to a car using bluetooth. Now, should drivers be able to use their phones in the car while driving.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Lead</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    There are some really good uses for using the bluetooth phone hookup. For one in Georgia people are not allowed to drive if they have their phone in their hand. This is where the bluetooth phone hookup comes in handy because you can do in the car phone calls to work or to somewhere else.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    This feature can come in really handy because if someone takes a day off of work and someone from their work calls them and starts to ask questions they can answer those questions right then and there.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    That way if someone was ever needed to give someone some advice in their place of work they could just click a button and give them the advice over the phone.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    There is also another use for the phone hookup to the car feature and that is so that someone can get notified if they do happen to have a family emergency.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    This is so that if someone does happen to have a family emergency and they are in the car already all they have to do is drive over to where the family emergency actually is. This is so that you can get notified if something bad does happen to someone's family member they won't have to wait until they get home to find out what happened.\n",
       "\n",
       "Once again being that when they do find out instead of having to leave the house again to go and see what happened because this person is actually out and about all they have to do is go to the place of the emergency. This may be one of the sadder ways that a phone can be used for while driving but it still is one of the more important things.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: #80ffb4; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    The next reason for why people should be able to use their phone while driving is what if someone has to call the police.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Claim</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2adddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Using the bluetooth phone hookup feature that most cars have nowadays someone can call up the police and say that they need assistance with something. For instance let's say that someone has a very strong feeling that they are being stalked on the way home from work or somewhere else so they can then call the police for their assistance. This can also be a real life saver because this can also be used if someone's water breaks on the way to the hospital that way someone can go ahead and let the hospital know what is happening so someone won't have to wait. So, in short, phones can be used to save someone's life.\n",
       "\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Evidence</span>\n",
       "</mark>\n",
       "</br>\n",
       "<mark class=\"entity\" style=\"background: d4dd80; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Phone's have been useful since the time of their invention. From the ability to be able to talk to people from a long distance away to making sure that people can keep in touch with their work life. Next, being able to keep in touch with the family making sure to be kept in the loop if something bad does happen. Lastly, being able to call the police and other emergency services to save lives.\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Concluding Statement</span>\n",
       "</mark>\n",
       " \n",
       "<mark class=\"entity\" style=\"background: #2b7ff6; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Phones are a necessity in day to day life which includes on the road.   \n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Position</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "path = Path(ROOT_DIR+'train')\n",
    "\n",
    "examples = train['id'].sample(n=5, random_state=42).values.tolist()\n",
    "\n",
    "for ex in examples:\n",
    "    displacy_discourse_type_visualize(ex, train, path)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Mar 21 14:39:43 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 528.49       Driver Version: 528.49       CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ... WDDM  | 00000000:01:00.0  On |                  N/A |\n",
      "| 65%   49C    P8    16W / 170W |    641MiB / 12288MiB |      9%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A      2416    C+G   ...werToys.ColorPickerUI.exe    N/A      |\n",
      "|    0   N/A  N/A      6008    C+G   ...werToys.PowerLauncher.exe    N/A      |\n",
      "|    0   N/A  N/A      8752    C+G   C:\\Windows\\explorer.exe         N/A      |\n",
      "|    0   N/A  N/A      9240    C+G   ...\\PowerToys.FancyZones.exe    N/A      |\n",
      "|    0   N/A  N/A      9516    C+G   ...n1h2txyewy\\SearchHost.exe    N/A      |\n",
      "|    0   N/A  N/A      9544    C+G   ...artMenuExperienceHost.exe    N/A      |\n",
      "|    0   N/A  N/A      9580    C+G   ...icrosoft VS Code\\Code.exe    N/A      |\n",
      "|    0   N/A  N/A     10408    C+G   ...lPanel\\SystemSettings.exe    N/A      |\n",
      "|    0   N/A  N/A     11384    C+G   ...cw5n1h2txyewy\\LockApp.exe    N/A      |\n",
      "|    0   N/A  N/A     12020    C+G   ...y\\ShellExperienceHost.exe    N/A      |\n",
      "|    0   N/A  N/A     12036    C+G   ...me\\Application\\chrome.exe    N/A      |\n",
      "|    0   N/A  N/A     12992    C+G   ...e\\PhoneExperienceHost.exe    N/A      |\n",
      "|    0   N/A  N/A     13992    C+G   ...661.44\\msedgewebview2.exe    N/A      |\n",
      "|    0   N/A  N/A     21424    C+G   C:\\Windows\\explorer.exe         N/A      |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
      "loading configuration file config.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\config.json\n",
      "Model config LongformerConfig {\n",
      "  \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
      "  \"attention_mode\": \"longformer\",\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"attention_window\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"ignore_attention_mask\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 4098,\n",
      "  \"model_type\": \"longformer\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"onnx_export\": false,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"sep_token_id\": 2,\n",
      "  \"transformers_version\": \"4.26.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading file vocab.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\vocab.json\n",
      "loading file merges.txt from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\merges.txt\n",
      "loading file tokenizer.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\tokenizer.json\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at None\n",
      "loading file tokenizer_config.json from cache at None\n",
      "loading configuration file config.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\config.json\n",
      "Model config LongformerConfig {\n",
      "  \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
      "  \"attention_mode\": \"longformer\",\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"attention_window\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"ignore_attention_mask\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 4098,\n",
      "  \"model_type\": \"longformer\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"onnx_export\": false,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"sep_token_id\": 2,\n",
      "  \"transformers_version\": \"4.26.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading configuration file config.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\config.json\n",
      "Model config LongformerConfig {\n",
      "  \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
      "  \"attention_mode\": \"longformer\",\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"attention_window\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"ignore_attention_mask\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 4098,\n",
      "  \"model_type\": \"longformer\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"onnx_export\": false,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"sep_token_id\": 2,\n",
      "  \"transformers_version\": \"4.26.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file tf_model.h5 from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\tf_model.h5\n",
      "Input ids are automatically padded from 5 to 512 to be a multiple of `config.attention_window`: 512\n",
      "Input ids are automatically padded from 5 to 512 to be a multiple of `config.attention_window`: 512\n",
      "Some layers from the model checkpoint at allenai/longformer-base-4096 were not used when initializing TFLongformerModel: ['lm_head']\n",
      "- This IS expected if you are initializing TFLongformerModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFLongformerModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the layers of TFLongformerModel were initialized from the model checkpoint at allenai/longformer-base-4096.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFLongformerModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "\n",
    "config = AutoConfig.from_pretrained(MODEL_NAME) \n",
    "\n",
    "backbone = TFAutoModel.from_pretrained(MODEL_NAME, config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single strategy\n"
     ]
    }
   ],
   "source": [
    "strategy = multi_gpu_use()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Enable Mixed precision\n",
    "\n",
    "When using Kaggle's P100 GPU, mixed precision does not make a difference. However, if you run this code locally on a recent GPU, using mixed precision, get speed up train time by as much as 2x.\n",
    "\n",
    "Mixed-precision training is a technique for training deep neural networks using both lower-precision (e.g., half-precision) and higher-precision (e.g., single-precision) floating-point numbers. This technique can lead to significant speedups in training and can reduce memory usage, especially when training large models with many layers.\n",
    "\n",
    "This line of code, the \"auto_mixed_precision\" option enables TensorFlow to automatically select the best precision format for each tensor during training, based on the tensor's dynamic range - without requiring manual specification of precision formats for each tensor, which can be time-consuming and error-prone.\n",
    "\n",
    "Further, mixed-precision training can also improve the numerical stability of the training process. Specifically, using lower-precision numbers can help avoid numerical underflow, which can occur when using single-precision numbers in deep neural networks with many layers. By using mixed-precision training, the numerical stability of the training process can be improved without sacrificing performance.\n",
    "\n",
    "However, it’s important to note that not all hardware supports mixed-precision training, and not all models may benefit from it. Therefore, it’s important to experiment with different precision formats and configurations to determine the optimal approach for a particular model and hardware setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "tf.config.optimizer.set_experimental_options({\"auto_mixed_precision\": True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train labels are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Lead', 'Position', 'Evidence', 'Claim', 'Concluding Statement',\n",
       "       'Counterclaim', 'Rebuttal'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The train labels are:')\n",
    "train.discourse_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 136 train texts.\n"
     ]
    }
   ],
   "source": [
    "unique_doc_ids = train.id.unique()\n",
    "\n",
    "print('There are',len(unique_doc_ids),'train texts.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Tokenize Train\n",
    "\n",
    "Code in the next cell is for converting Kaggle's train dataset into a NER token array that we can use to train a NER transformer. \n",
    "\n",
    "\n",
    "---------------------\n",
    "\n",
    "## B_ and I_ are used to indicate the boundary of a named entity.\n",
    "\n",
    "In NER, the prefix B_ is used to indicate the beginning of a named entity, and is typically followed by a label indicating the type of entity (e.g. B_PERSON for the first token in a person's name). The prefix I_ is used to indicate tokens that are inside a named entity, and is also followed by the entity type (e.g. I_PERSON for the second and subsequent tokens in a person's name). \n",
    "\n",
    "In NER, the goal is to identify and classify named entities (such as people, organizations, and locations) in a text. To do this, NER models often assign a label to each token in the input text, indicating whether the token is part of a named entity and, if so, what type of entity it is.\n",
    "\n",
    "For example, consider the sentence \"John Smith is the CEO of Acme Corporation\". A NER model might output the following sequence of labels:\n",
    "\n",
    "```\n",
    "B_PERSON   I_PERSON    O   O  O   B_ORGANIZATION    I_ORGANIZATION\n",
    "John      Smith       is  the CEO of              Acme          Corporation\n",
    "```\n",
    "\n",
    "Here, B_PERSON and I_PERSON are used to mark the tokens that make up the person's name, while B_ORGANIZATION and I_ORGANIZATION mark the tokens that make up the organization name.\n",
    "\n",
    "------------------------------------------------\n",
    "\n",
    "In this NB, the B label is the start and I labels are all the middles and the end. \n",
    "\n",
    "Lets take an example below. \n",
    "\n",
    "In below, the square brackets designate different tokens. And the total text is 4-sentences long, where sentence-1 is the \"position\", sentence-2 nothing, sentence-3 is \"evidence\" and sentence-4 is a another \"evidence\".\n",
    "\n",
    "```\n",
    "[This ][is ][sentence ][one. ][This ][is ][sentence ][two. ][This ][is ][sentence ][three. ][This ][is ][sentence ][four. ]\n",
    "\n",
    "```\n",
    "Then the prediction from our model is as follows:\n",
    "\n",
    "```\n",
    "[2][3][3][3] [14][14][14][14] [4][5][5][5] [4][5][5][5]\n",
    "\n",
    "```\n",
    "Total, there are 16 tokens so our model makes 16 predictions. Each prediction is a softmax class from 0 thru 14. The prediction [2] is position-B, the prediction [3] is position-I. The prediction [14] is O which means it is no class. The prediction [4] is evidence-B, the prediction [5] is evidence-I.\n",
    "\n",
    "Notice how each span begins with a class-B and continues with class-I. When we see a new class-B, we know that we are starting a new span.\n",
    "\n",
    "The `calculate_preds()` divides each prediction by 2 and then looks for [1][1.5][1.5][1.5] [7][7][7][7] [2][2.5][2.5][2.5] [2][2.5][2.5][2.5])\n",
    "\n",
    "\n",
    "-------------------------------------------------\n",
    "\n",
    "## In below Why do we need 2 arrays for each class? LEAD_b and LEAD_i for example?\n",
    "\n",
    "Its to capture the start and continuation of tokens. `LEAD_b` is the start token e.g. and `LEAD_i` is every other token including the middle tokens and end token for the LEAD span."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
      "loading configuration file config.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\config.json\n",
      "Model config LongformerConfig {\n",
      "  \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
      "  \"attention_mode\": \"longformer\",\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"attention_window\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"ignore_attention_mask\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 4098,\n",
      "  \"model_type\": \"longformer\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"onnx_export\": false,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"sep_token_id\": 2,\n",
      "  \"transformers_version\": \"4.26.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading file vocab.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\vocab.json\n",
      "loading file merges.txt from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\merges.txt\n",
      "loading file tokenizer.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\tokenizer.json\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at None\n",
      "loading file tokenizer_config.json from cache at None\n",
      "loading configuration file config.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\config.json\n",
      "Model config LongformerConfig {\n",
      "  \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
      "  \"attention_mode\": \"longformer\",\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"attention_window\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"ignore_attention_mask\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 4098,\n",
      "  \"model_type\": \"longformer\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"onnx_export\": false,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"sep_token_id\": 2,\n",
      "  \"transformers_version\": \"4.26.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "MAX_LEN = 1024\n",
    "# means that each token array will have a maximum length of 1024 tokens.\n",
    "\n",
    "# initialize the tokenizer object \n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "\n",
    "train_tokens = np.zeros((len(unique_doc_ids),MAX_LEN), dtype='int32')\n",
    "# train_tokens array is of shape (len(unique_doc_ids), MAX_LEN). \n",
    "# This array will store the tokenized versions of the text documents of train dataset\n",
    "\n",
    "train_attention = np.zeros((len(unique_doc_ids),MAX_LEN), dtype='int32')\n",
    "# train_attention has the same shape as train_tokens. \n",
    "# This array will store the attention masks for each tokenized document  of train dataset\n",
    "\n",
    "# Declare 2 arrays for each class\n",
    "# Threre are a total of 14 classes\n",
    "B_Lead = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "I_Lead = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "B_Position = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "I_Position = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "B_Evidence = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "Evidence_I = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "B_Claim = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "I_Claim = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "B_Conclusion = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "I_Conclusion = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "B_Counterclaim = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "I_Counterclaim = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "B_Rebuttal = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "I_Rebuttal = np.zeros((len(unique_doc_ids),MAX_LEN))\n",
    "\n",
    "# lets define some helper vars\n",
    "train_lens = []\n",
    "\n",
    "targets_b = [B_Lead, B_Position, B_Evidence, B_Claim, B_Conclusion, B_Counterclaim, B_Rebuttal]\n",
    "\n",
    "targets_i = [I_Lead, I_Position, Evidence_I, I_Claim, I_Conclusion, I_Counterclaim, I_Rebuttal]\n",
    "\n",
    "target_id_map = {'Lead':0, 'Position':1, 'Evidence':2, 'Claim':3, 'Concluding Statement':4,\n",
    "             'Counterclaim':5, 'Rebuttal':6}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`targets_b` and `targets_i` arrays are used to store the ground truth labels for the beginning and intermediate tokens of each named entity class in the tokenized input. Each class has its own pair of arrays (`B_*` and `I_*`), with the same shape as `train_tokens` and `train_attention` arrays.\n",
    "    \n",
    "Align with tokenized input and attention masks: By having the same shape as `train_tokens` and `train_attention` arrays, the `targets_b` and `targets_i` arrays ensure that the ground truth labels are aligned with the tokenized input and attention masks. This alignment is essential for training the model, as it helps the model learn to map the tokenized input to the correct named entity labels.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 , 100 , "
     ]
    }
   ],
   "source": [
    "prepare_data(train, unique_doc_ids,train_lens, tokenizer, train_tokens, train_attention, ROOT_DIR, MAX_LEN, target_id_map, targets_b, targets_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-05T19:18:41.254514Z",
     "iopub.status.busy": "2023-03-05T19:18:41.254244Z",
     "iopub.status.idle": "2023-03-05T19:18:41.738695Z",
     "shell.execute_reply": "2023-03-05T19:18:41.737800Z",
     "shell.execute_reply.started": "2023-03-05T19:18:41.254484Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(train_lens,bins=100)\n",
    "# In the prepare_data() method, train_lens is a list that holds the lengths (number of words) of each original text document in the training dataset. The line train_lens.append(len(txt.split())) appends the length of the current document to the train_lens list.\n",
    "plt.title('Histogram of Train Word Counts',size=16)\n",
    "plt.xlabel('Train Word Count',size=14)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "From the histogram of train word counts above, we see that using a transformer width of 1024 is a good enough for capturing most of the data's signal and also not having too large a model.\n",
    "\n",
    "We could probably explore other widths between 512 and 1024 also. Or we could use widths of size 512 or smaller and use a stride which breaks a single text into multiple chunks (with possible overlap).\n",
    "\n",
    "-------------------------------------------------------------------------\n",
    "\n",
    "## Build a Tensor (with interleaving structure) to represent 15 classes of Targets\n",
    "\n",
    "Now why do we need 15 classes - Could we not use 8 classes instead of those 15 classes?\n",
    "\n",
    "Basically, we could use 8 if we do ONLY \"IO\" NER tagging formats for all the NERs here. \n",
    "\n",
    "But we are doing \"BIO\". So each of the seven classes needs 1 \"B\" and each of seven classes needs 1 \"I\" and then the 15th class is \"O\".\n",
    "\n",
    "Background on BIO/IO - In an NLP/NER project, IO and BIO are two different tagging formats for tagging tokens in a chunking task. BIO (short for Beginning, Inside, Outside) format is a common tagging format where the B- prefix before a tag indicates that the tag is the beginning of a chunk, and an I- prefix before a tag indicates that the tag is inside a chunk. \n",
    "On the other hand, IO (short for Inside, Outside) is another tagging format that is easy to implement but should be worse than IOBES for NER tasks\n",
    "\n",
    "There are several other tagging formats used in NLP/NER projects. Some of these include IOBES (short for Beginning, Inside, Outside, End, Single) and BILOU (short for Beginning, Inside, Last, Outside, and Unit) . These tagging formats help to distinguish between the end of a named entity and single entities."
   ]
  },
  {
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   "execution_count": 18,
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   "source": [
    "#  initializes a 3D NumPy array called targets with zeros\n",
    "# The purpose of this array is to store target labels for each unique document and for each token (word or character) up to the maximum length (MAX_LEN) specified. The depth of 15 layers is used to store different label information for each token.\n",
    "targets = np.zeros((len(unique_doc_ids),MAX_LEN,15), dtype='int32')\n",
    "\n",
    "# In below loop, for each class (k i.e. each of the 7 classes Lead, Position, Evidence, Claim, Conclusion, Counterclaim, and Rebuttal), the code assigns the corresponding \"beginning\" (targets_b) and \"inside\" (targets_i) labels to the 'targets' 3D NumPy array in an interleaved fashion. \n",
    "# --------------\n",
    "# For each class, it assigns the \"beginning\" labels (targets_b) to the even indices (2 * k) and the \"inside\" labels (targets_i) to the odd indices (2 * k + 1) of the 'targets' array. \n",
    "# The result is an interleaved structure that allows for efficient storage and retrieval of target labels for each component during the training and evaluation of the NLP model.\n",
    "# So, the 'targets' array will store the \"beginning\" label for class k at depth 2 * k and the \"inside\" label for class k at depth 2 * k + 1.\n",
    "for k in range(7):\n",
    "    targets[:,:,2*k] = targets_b[k]\n",
    "    targets[:,:,2*k+1] = targets_i[k]\n",
    "# The for loop iterates 7 times and sets the values of the even-numbered columns (0, 2, 4, etc.) to the values of the targets for the beginning of entities (targets_b) for each entity type. It sets the values of the odd-numbered columns (1, 3, 5, etc.) to the values of the targets for the inside of entities (targets_i) for each entity type.\n",
    "\n",
    "targets[:,:,14] = 1-np.max(targets,axis=-1)\n",
    "# Finally, set the values of the last column to 1 minus the maximum value of the targets in the third dimension. This column corresponds to the \"outside\" of any entities in the text, meaning it is not part of any entity. This is done to ensure that the model can distinguish between entities and non-entities in the text.\n",
    "# np.max(targets, axis=-1): This calculates the maximum value along the last axis. The axis=-1 notation is shorthand for selecting the last axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "targets : [[[0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]]\n",
      "\n",
      " [[0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]]\n",
      "\n",
      " [[0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]]\n",
      "\n",
      " [[0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]]\n",
      "\n",
      " [[0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]\n",
      "  [0 0 0 ... 0 0 1]]]\n"
     ]
    }
   ],
   "source": [
    "print(\"targets :\", targets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "targets.shape : (136, 1024, 15)\n"
     ]
    }
   ],
   "source": [
    "print(\"targets.shape :\", targets.shape)\n",
    "# targets.shape : (136, 1024, 15)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------\n",
    "\n",
    "## Why exactly the `targets` array is built like this with even and odd positioning of target labels\n",
    "\n",
    "Interleaving structures for target labels, like the one used here, are quite common in deep learning NLP projects, particularly for sequence labeling tasks and especially when handling a multi-class problem with multiple entity types.\n",
    "\n",
    "This structure has a couple of benefits for the NLP/NER model training and evaluation:\n",
    "\n",
    "**Compact representation**: By interleaving the \"beginning\" and \"inside\" labels, the targets array effectively stores information about the named entities in a compact manner. This allows the model to have a single array to reference when it needs information about the labels, simplifying the process of accessing and updating the target labels during training and evaluation.\n",
    "\n",
    "**Easier indexing:** The even and odd positioning makes it easy to index and retrieve the \"beginning\" and \"inside\" labels for a specific argument component. You can directly access the \"beginning\" label for the k-th component using the index 2 * k and the \"inside\" label using the index 2 * k + 1. This clear, mathematical relationship between the component index and the target label position simplifies the process of working with the target labels during model training and evaluation.\n",
    "\n",
    "**Flexibility:** The interleaved structure allows for the possibility of extending the model to support additional types of labels or components in the future. You can add new components by simply extending the depth of the targets array and updating the indices accordingly.\n",
    "\n",
    "So my final 'target' variable will be of the following form\n",
    "\n",
    "```\n",
    "targets = [\n",
    "    [\n",
    "        [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # Token 0: B_Lead\n",
    "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # Token 1: I_Lead\n",
    "        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # Token 2: B_Position\n",
    "        [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # Token 3: I_Position\n",
    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   # Token 4: No labels\n",
    "    ],\n",
    "    [\n",
    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # Token 0: No labels\n",
    "        [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],  # Token 1: B_Evidence\n",
    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],  # Token 2: I_Evidence\n",
    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # Token 3: No labels\n",
    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]   # Token 4: B_Conclusion\n",
    "    ]\n",
    "]\n",
    "```\n",
    "\n",
    "The 15 columns in each innermost array represent the interleaved \"beginning\" and \"inside\" labels for the 7 classes or entity-types, as well as an additional column for tokens with no labels (if applicable)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why this line is needed - `targets[:,:,14] = 1-np.max(targets,axis=-1)`\n",
    "\n",
    "The line sets the values of the last column of the targets array to 1 minus the maximum value along the third axis (i.e., the axis representing the 15 entity types). This is done to ensure that there is a \"non-entity\" label in the target labels.\n",
    "\n",
    "In NER tasks, the objective is to predict named entities in the input text, and the labels for each token in the text indicate whether it is the beginning or continuation of an entity, or not part of any entity at all. \n",
    "\n",
    "In this case, there are 7 entity types, and each entity has a beginning label and a continuation label. That means there are 14 columns of target labels representing the 7 entity types.\n",
    "\n",
    "The 15th i.e. the last column in targets (i.e., column 14) represents the \"non-entity\" label. **The values in this column should be 1 if the token is not part of any entity, and 0 otherwise.** \n",
    "\n",
    "The expression `1-np.max(targets,axis=-1)` computes the maximum value of the target labels along the third axis, and subtracts it from 1. \n",
    "\n",
    "### This means that the value in the \"non-entity\" column will be 1 if there are no entity labels (i.e., the maximum value is 0), and 0 otherwise.\n",
    "\n",
    "So overall, by setting the values in the last column of targets to `1 minus the maximum value of the target labels`, the code ensures that there is a \"non-entity\" label in the targets, and that the model can learn to distinguish between named entities and non-entities in the input text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-05T19:18:44.789079Z",
     "iopub.status.busy": "2023-03-05T19:18:44.788445Z",
     "iopub.status.idle": "2023-03-05T19:18:45.619557Z",
     "shell.execute_reply": "2023-03-05T19:18:45.618741Z",
     "shell.execute_reply.started": "2023-03-05T19:18:44.789045Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved NER tokens\n"
     ]
    }
   ],
   "source": [
    "np.save(f'targets_{MAX_LEN}', targets)\n",
    "np.save(f'tokens_{MAX_LEN}', train_tokens)\n",
    "np.save(f'attention_{MAX_LEN}', train_attention)\n",
    "print('Saved NER tokens')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Build Model\n",
    "We will use LongFormer backbone and add our own NER head using one hidden layer of size 256 and one final layer with softmax. We use 15 classes because we have a `B` class and `I` class for each of 7 labels. And we have an additional class (called `O` class) for tokens that do not belong to one of the 14 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
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    },
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\config.json\n",
      "Model config LongformerConfig {\n",
      "  \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
      "  \"attention_mode\": \"longformer\",\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"attention_window\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"ignore_attention_mask\": false,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 4098,\n",
      "  \"model_type\": \"longformer\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"onnx_export\": false,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"sep_token_id\": 2,\n",
      "  \"transformers_version\": \"4.26.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file tf_model.h5 from cache at C:\\Users\\PC60/.cache\\huggingface\\hub\\models--allenai--longformer-base-4096\\snapshots\\7a2d50a05513897192e79ea7c750493795d7cec0\\tf_model.h5\n",
      "Input ids are automatically padded from 5 to 512 to be a multiple of `config.attention_window`: 512\n",
      "Input ids are automatically padded from 5 to 512 to be a multiple of `config.attention_window`: 512\n",
      "Some layers from the model checkpoint at allenai/longformer-base-4096 were not used when initializing TFLongformerModel: ['lm_head']\n",
      "- This IS expected if you are initializing TFLongformerModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFLongformerModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the layers of TFLongformerModel were initialized from the model checkpoint at allenai/longformer-base-4096.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFLongformerModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "with strategy.scope():\n",
    "    model = build_model(MODEL_NAME, MAX_LEN)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Train or Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-05T19:19:05.963679Z",
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     "shell.execute_reply.started": "2023-03-05T19:19:05.963629Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "EPOCHS = 4\n",
    "BATCH_SIZE = 2\n",
    "\n",
    "# Below learning rates, I am setting only for running the entire NB fast\n",
    "# For better result, you should experiement with even lower learning rates\n",
    "LRS = [.0001, .0002]\n",
    "\n",
    "def learning_func(epoch):\n",
    "    return LRS[epoch]\n",
    "\n",
    "lr_callback = tf.keras.callbacks.LearningRateScheduler(learning_func, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-05T19:19:05.970759Z",
     "iopub.status.busy": "2023-03-05T19:19:05.970473Z",
     "iopub.status.idle": "2023-03-05T19:19:05.993525Z",
     "shell.execute_reply": "2023-03-05T19:19:05.992814Z",
     "shell.execute_reply.started": "2023-03-05T19:19:05.970725Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size 122 , Valid size 14\n"
     ]
    }
   ],
   "source": [
    "# Split the data with train (90%) and valid(10%)\n",
    "np.random.seed(42)\n",
    "train_idx = np.random.choice(np.arange(len(unique_doc_ids)),int(0.9*len(unique_doc_ids)),replace=False)\n",
    "\n",
    "valid_idx = np.setdiff1d(np.arange(len(unique_doc_ids)),train_idx)\n",
    "# np.setdiff1d() returns an array of indices that are not present in the train_idx array. \n",
    "\n",
    "np.random.seed(None)\n",
    "\n",
    "print('Train size',len(train_idx),', Valid size',len(valid_idx))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1: LearningRateScheduler setting learning rate to 0.01.\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_longformer_model_1/longformer/pooler/dense/kernel:0', 'tf_longformer_model_1/longformer/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
      "WARNING:tensorflow:Gradients do not exist for variables ['tf_longformer_model_1/longformer/pooler/dense/kernel:0', 'tf_longformer_model_1/longformer/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
      "61/61 - 70s - loss: nan - categorical_accuracy: 0.0083 - val_loss: nan - val_categorical_accuracy: 8.3705e-04 - lr: 0.0100 - 70s/epoch - 1s/step\n"
     ]
    }
   ],
   "source": [
    "model.fit(x=[train_tokens[train_idx,], train_attention[train_idx,]],\n",
    "              y=targets[train_idx,],\n",
    "              validation_data=([train_tokens[valid_idx,], train_attention[valid_idx,]],\n",
    "                               targets[valid_idx,]),\n",
    "              callbacks=[lr_callback],\n",
    "              epochs=EPOCHS,\n",
    "              batch_size=BATCH_SIZE,\n",
    "              verbose=2,\n",
    "              )\n",
    "model.save_weights(f'longf_train_model')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Validate Model - Infer OOF\n",
    "We will now make predictions on the validation texts. Our model makes label predictions for each token, we need to convert this into a list of word indices for each label. Note that the tokens and words are not the same. A single word may be broken into multiple tokens. Therefore we need to first create a map to change token indices to word indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 - 13s - 13s/epoch - 13s/step\n",
      "OOF predictions shape: (14, 1024, 15)\n"
     ]
    }
   ],
   "source": [
    "p = model.predict([train_tokens[valid_idx,], train_attention[valid_idx,]], \n",
    "                  batch_size=16, verbose=2)\n",
    "\n",
    "print('OOF predictions shape:',p.shape)\n",
    "\n",
    "oof_preds = np.argmax(p,axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 1024)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_preds.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanation for `oof_preds = np.argmax(p,axis=-1)`\n",
    "\n",
    "In the given code snippet for an NER (Named Entity Recognition) / NLP (Natural Language Processing) project, the line `oof_preds = np.argmax(p, axis=-1)` is used for extracting the most likely predicted class for each token in the input sequence.\n",
    "\n",
    "Let's break down the line and explain each part:\n",
    "\n",
    "1. `np.argmax()`: will find the index (position) of the maximum value along a specified axis in an array (in this case, the prediction array `p`). The maximum value corresponds to the highest probability of a token belonging to a certain class (e.g., entity type like person, location, organization, etc.).\n",
    "\n",
    "2. `p`: The prediction array, which has the shape `(number_of_samples, sequence_length, number_of_classes)`. (Refer to the print statement above for `p.shape`)\n",
    "\n",
    "Each element `p[i, j, k]` represents the probability that the token at position `j` in the input sequence `i` belongs to class `k`.\n",
    "\n",
    "3. `axis=-1`:By setting `axis=-1`, we're looking for the maximum value along the last axis, which corresponds to the class probabilities. This means we're finding the class with the highest probability for each token in every input sequence.\n",
    "\n",
    "So, `oof_preds = np.argmax(p, axis=-1)` results in an array with the shape `(number_of_samples, sequence_length)`, where each element `oof_preds[i, j]` is the index (class label) of the most likely class for the token at position `j` in the input sequence `i`.\n",
    "\n",
    "`oof_preds` will be used in the project to evaluate the model's performance by comparing its predictions with the ground truth labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "target_id_map_updated = {0: 'Lead', 1: 'Position', 2: 'Evidence', 3: 'Claim',\n",
    "                         4: 'Concluding Statement', 5: 'Counterclaim', 6: 'Rebuttal', 7: 'blank'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>class</th>\n",
       "      <th>predictionstring</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>019328A0D7A3</td>\n",
       "      <td>Lead</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>019328A0D7A3</td>\n",
       "      <td>Lead</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>019328A0D7A3</td>\n",
       "      <td>Lead</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>019328A0D7A3</td>\n",
       "      <td>Lead</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>019328A0D7A3</td>\n",
       "      <td>Lead</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id class predictionstring\n",
       "0  019328A0D7A3  Lead                0\n",
       "1  019328A0D7A3  Lead                1\n",
       "2  019328A0D7A3  Lead                2\n",
       "3  019328A0D7A3  Lead                2\n",
       "4  019328A0D7A3  Lead                2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_of_fold = calculate_preds(tokenizer, target_id_map_updated, MAX_LEN, ROOT_DIR, text_ids=unique_doc_ids[valid_idx], preds=oof_preds, dataset='train', verbose=True)\n",
    "\n",
    "out_of_fold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following classes are present in out_of_fold preds:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Lead'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The following classes are present in out_of_fold preds:')\n",
    "out_of_fold['class'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Validation Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "valid = train.loc[train['id'].isin(unique_doc_ids[valid_idx])]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This line of code is selecting a subset of rows from the 'train' dataframe based on a condition. Specifically, it is selecting the rows where the value in the 'id' column is present in a list of unique document ids that are part of the validation set.\n",
    "\n",
    "Here's a breakdown of the code:\n",
    "\n",
    "1. `train['id']` selects the 'id' column from the 'train' dataframe.\n",
    "2. `unique_doc_ids[valid_idx]` selects a subset of unique document ids from the validation set, based on the indices in the 'valid_idx' list.\n",
    "3. `train['id'].isin(unique_doc_ids[valid_idx])` creates a boolean Series, which is True for rows where the 'id' value is present in the selected subset of unique document ids.\n",
    "4. `train.loc[]` selects rows from the 'train' dataframe based on the boolean Series created in the previous step. This creates a new dataframe called 'valid' which contains only the rows from 'train' where the 'id' value is present in the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lead 0.0\n",
      "Overall 0.0\n"
     ]
    }
   ],
   "source": [
    "f1s = []\n",
    "CLASSES = out_of_fold['class'].unique()\n",
    "\n",
    "for c in CLASSES:\n",
    "    pred_df = out_of_fold.loc[out_of_fold['class']==c].copy()\n",
    "    gt_df = valid.loc[valid['discourse_type']==c].copy()\n",
    "    f1 = compute_macro_f1_score(pred_df, gt_df)\n",
    "    print(c,f1)\n",
    "    f1s.append(f1)\n",
    "\n",
    "\n",
    "print('Overall',np.mean(f1s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Infer Test Data\n",
    "We will now infer the test data and create a submission. Our CV is 0.633, let's see what our LB is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5 test texts.\n"
     ]
    }
   ],
   "source": [
    "# GET TEST TEXT IDS\n",
    "files = os.listdir(ROOT_DIR+'test')\n",
    "\n",
    "TEST_IDS = [f.replace('.txt','') for f in files if 'txt' in f]\n",
    "\n",
    "print('There are',len(TEST_IDS),'test texts.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name  ../input/test/0FB0700DAF44.txt\n",
      "name  ../input/test/18409261F5C2.txt\n",
      "name  ../input/test/D46BCB48440A.txt\n",
      "name  ../input/test/D72CB1C11673.txt\n",
      "name  ../input/test/DF920E0A7337.txt\n"
     ]
    }
   ],
   "source": [
    "# First, converting text into tokens\n",
    "test_tokens = np.zeros((len(TEST_IDS),MAX_LEN), dtype='int32')\n",
    "test_attention = np.zeros((len(TEST_IDS),MAX_LEN), dtype='int32')\n",
    "\n",
    "# I need to read and tokenize the text files before applying the trained model\n",
    "for id_num in range(len(TEST_IDS)):        \n",
    "    n = TEST_IDS[id_num]\n",
    "    name = f'{ROOT_DIR}test/{n}.txt'\n",
    "    print('name ', name)\n",
    "    txt = open(name, 'r').read()\n",
    "    tokens = tokenizer.encode_plus(txt, max_length=MAX_LEN, padding='max_length',\n",
    "                                   truncation=True, return_offsets_mapping=True)\n",
    "    test_tokens[id_num,] = tokens['input_ids']\n",
    "    test_attention[id_num,] = tokens['attention_mask']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 - 0s - 405ms/epoch - 405ms/step\n",
      "Test predictions shape: (5, 1024, 15)\n"
     ]
    }
   ],
   "source": [
    "# Finally inference\n",
    "p = model.predict([test_tokens, test_attention], \n",
    "                  batch_size=16, verbose=2)\n",
    "print('Test predictions shape:',p.shape)\n",
    "test_preds = np.argmax(p,axis=-1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "`test_preds` will be used for the final evaluate of the model's performance by comparing its predictions with the ground truth labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Write Submission CSV"
   ]
  },
  {
   "cell_type": "code",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>class</th>\n",
       "      <th>predictionstring</th>\n",
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      "text/plain": [
       "             id class predictionstring\n",
       "0  0FB0700DAF44  Lead                0\n",
       "1  0FB0700DAF44  Lead                1\n",
       "2  0FB0700DAF44  Lead                2\n",
       "3  0FB0700DAF44  Lead                3\n",
       "4  0FB0700DAF44  Lead                3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the predictions for test files\n",
    "\n",
    "inference_df = calculate_preds(tokenizer, target_id_map_updated, MAX_LEN, ROOT_DIR, text_ids=TEST_IDS, preds=test_preds, dataset='test', verbose=False)\n",
    "\n",
    "inference_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# WRITE SUBMISSION CSV\n",
    "inference_df.to_csv('submission.csv',index=False)"
   ]
  }
 ],
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